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themanas021
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2c87ef6
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Parent(s):
f127d58
Create app.py
Browse files
app.py
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import streamlit as st
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import cv2
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from PIL import Image as PilImage
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from PIL import ImageDraw
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import numpy as np
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import io
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import base64
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# Load the face detection classifier
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face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
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def detect_and_write_on_faces(image):
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text_to_write = "Happy faces, this person has a retention rate of 69% in a balanced way."
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# Convert the uploaded image to grayscale for face detection
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# Detect faces in the image
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faces = face_cascade.detectMultiScale(gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))
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# Convert the OpenCV image to a Pillow image
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pil_image = PilImage.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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draw = ImageDraw.Draw(pil_image)
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for (x, y, w, h) in faces:
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# Write text on the detected face
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draw.text((x, y - 10), text_to_write, fill=(255, 0, 0, 0))
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# Convert the Pillow image back to OpenCV format
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image_with_text = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR)
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return image_with_text
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st.title("Face Detection and Text Writing")
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uploaded_image = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"])
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if uploaded_image is not None:
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if st.button("Process Image"):
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input_image = cv2.imdecode(np.frombuffer(uploaded_image.read(), np.uint8), -1)
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result_image = detect_and_write_on_faces(input_image)
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st.image(result_image, caption="Processed Image", use_column_width=True)
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# Allow the user to download the processed image as a JPEG file
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output_buffer = io.BytesIO()
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PilImage.fromarray(result_image).save(output_buffer, format="JPEG")
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st.markdown("### Download Processed Image")
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st.markdown(
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f"Download your processed image [here](data:file/jpeg;base64,{base64.b64encode(output_buffer.getvalue()).decode()})"
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)
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